The present invention relates generally to magnetic resonance imaging (“MRI”) methods and systems and, more particularly, the invention relates to a system and method for the compression, in hardware, of acquired MR data.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but presses about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically proven pulse sequences and they also enable the development of new pulse sequences.
The MR signals acquired with an MRI system are signal samples of the subject of the examination in Fourier space, or what is often referred to in the art as “k-space”. Each MR measurement cycle, or pulse sequence, typically samples a portion of k-space along a sampling trajectory characteristic of that pulse sequence. Most pulse sequences sample k-space in a raster scan-like pattern sometimes referred to as a “spin-warp”, a “Fourier”, a “rectilinear” or a “Cartesian” scan. The spin-warp scan technique is discussed in an article entitled “Spin-Warp MR Imaging and Applications to Human Whole-Body Imaging” by W. A. Edelstein et al., Physics in Medicine and Biology, Vol. 25, pp. 751-756 (1980). It employs a variable amplitude phase encoding magnetic field gradient pulse prior to the acquisition of MR spin-echo signals to phase encode spatial information in the direction of this gradient. In a two-dimensional implementation (2DFT), for example, spatial information is encoded in one direction by applying a phase encoding gradient (Gy) along that direction, and then a spin-echo signal is acquired in the presence of a readout magnetic field gradient (Gx) in a direction orthogonal to the phase encoding direction. The readout gradient present during the spin-echo acquisition encodes spatial information in the orthogonal direction. In a typical 2DFT pulse sequence, the magnitude of the phase encoding gradient pulse Gy is incremented (ΔGy) in the sequence of measurement cycles, or “views” that are acquired during the scan to produce a set of k-space MR data from which an entire image can be reconstructed.
There are many other k-space sampling patterns used by MRI systems These include “radial”, or “projection reconstruction” scans in which k-space is sampled as a set of radial sampling trajectories extending from the center of k-space as described, for example, in U.S. Pat. No. 6,954,067. The pulse sequences for a radial scan are characterized by the lack of a phase encoding gradient and the presence of a readout gradient that changes direction from one pulse sequence view to the next. There are also many k-space sampling methods that are closely related to the radial scan and that sample along a curved k-space sampling trajectory rather than the straight line radial trajectory. Such pulse sequences are described, for example, in “Fast Three Dimensional Sodium Imaging”, MRM, 37:706-715, 1997 by F. E. Boada, et al. and in “Rapid 3D PC-MRA Using Spiral Projection Imaging”, Proc. Intl. Soc. Magn. Reson. Med. 13 (2005) by K. V. Koladia et al and “Spiral Projection Imaging: a new fast 3D trajectory”, Proc. Intl. Soc. Mag. Reson. Med. 13 (2005) by J. G. Pipe and Koladia.
An image is reconstructed from the acquired k-space data by transforming the k-space data set to an image space data set. There are many different methods for performing this task and the method used is often determined by the technique used to acquire the k-space data. With a Cartesian grid of k-space data that results from a 2D or 3D spin-warp acquisition, for example, the most common reconstruction method used is an inverse Fourier transformation (“2DFT” or “3DFT”) along each of the 2 or 3 axes of the data set. With a radial k-space data set and its variations, the most common reconstruction method includes “regridding” the k-space samples to create a Cartesian grid of k-space samples and then performing a 2DFT or 3DFT on the regridded k-space data set. In the alternative, a radial k-space data set can also be transformed to Radon space by performing a 1 DFT of each radial projection view and then transforming the Radon space data set to image space by performing a filtered backprojection.
Depending on the technique used, many MR scans currently used to produce medical images require many minutes to acquire the necessary data. The reduction of this scan time is an important consideration, since reduced scan time increases patient throughout, improves patient comfort, and improves image quality by reducing motion artifacts. Many different strategies have been developed to shorten the scan time.
One such strategy is referred to generally as “parallel imaging”. Parallel imaging techniques use spatial information from arrays of RF receiver coils to substitute for the encoding that would otherwise have to be obtained in a sequential fashion using RF pulses and field gradients (such as phase and frequency encoding). Each of the spatially independent receiver coils of the array carries certain spatial information and has a different sensitivity profile. This information is utilized in order to achieve a complete location encoding of the received MR signals by a combination of the simultaneously acquired data received from the separate coils. Specifically, parallel imaging techniques undersample k-space by reducing the number of acquired phase-encoded k-space sampling lines while keeping the maximal extent covered in k-space fixed. The combination of the separate MR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image (in comparison to conventional k-space data acquisition) by a factor that in the most favorable case equals the number of the receiver coils. Thus the use of multiple receiver coils acts to multiply imaging speed, without increasing gradient switching rates or RF power.
Two categories of such parallel imaging techniques that have been developed and applied to in vivo imaging are SENSE (SENSitivity Encoding) and SMASH (SiMultaneous Acquisition of Spatial Harmonics). With SENSE, the undersampled k-space data is first Fourier transformed to produce an aliased image from each coil, and then the aliased image signals are unfolded by a linear transformation of the superimposed pixel values. With SMASH, the omitted k-space lines are filled in or reconstructed prior to Fourier transformation, by constructing a weighted combination of neighboring lines acquired by the different receiver coils. SMASH requires that the spatial sensitivity of the coils be determined, and one way to do so is by “autocalibration” that entails the use of variable density k-space sampling.
A more recent advance to SMASH techniques using autocalibration is a technique known as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions), introduced by Griswold et al. This technique is described in U.S. Pat. No. 6,841,998 as well as in the article titled “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” by Griswold et al. and published in Magnetic Resonance in Medicine 47:1202-1210 (2002). Using these GRAPPA techniques, lines near the center of k-space are sampled at the Nyquist frequency (in comparison to the greater spaced lines at the edges of k-space). These so-called autocalibration signal (ACS) lines are then used to determine the weighting factors that are used to reconstruct the missing k-space lines. In particular, a linear combination of individual coil data is used to create the missing lines of k-space. The coefficients for the combination are determined by fitting the acquired data to the more highly sampled data near the center of k-space.
Large coil arrays, for example, coil arrays having 96 or more channels, provide improved sensitivity and increased acceleration capabilities. However, MR systems currently capable of using these large coil arrays, that is, MRI systems equipped with 96 or more receiver channels, are expensive and scarce. Furthermore, image reconstruction from so many channels of data is a computationally expensive process that is especially problematic for high-resolution and high-acceleration imaging, which are the type of applications for which large coil arrays are typically designed. Unaccelerated image reconstruction is readily parallelizable with one processor assigned to each channel followed by a simple combination method such as the pixel-by-pixel, “sum-of-squares” method. In contrast, the computational burden and difficulties in parallelizing reconstruction increase dramatically when a method, such as GRAPPA or SENSE, is used in which all of the multi-channel data is operated on simultaneously. With GRAPPA for example, the computation burden scales approximately as the cube of the number of channels. Therefore, the reconstruction of an accelerated 96-channel scan can easily take ten times longer than the reconstruction of an accelerated 32-channel scan. This is true even when using an expensive, high-end reconstruction computer, for example, a dual quad-core Opteron with 32 GB RAM running 64-bit Linux.
The data-rate limitation of the communication bus between a digital receiver and a reconstruction computer poses an additional problem when acquiring multi-channel MR data using large coil arrays. For example, the throughput limit of common bus technologies, such as PCI, is approached when performing a 128-channel scan with a fast pulse sequence, for example, a 2D 64×64 echo planar imaging (EPI) pulse sequence at 15 slices per second. Methods such as echo volumar imaging (EVI), which may use a 64×64×48 matrix at five volumes per second, can produce a 40-fold increase in data-throughput requirements that exceeds the limitations of common bus technologies.
The computational burden of image reconstruction can be reduced by employing a software-based compression scheme to compress multi-channel MR data prior to reconstruction. These software-based compression schemes may implement a technique known as mode-mixing, which decomposes and transforms multi-channel data so that the signal-to-noise ratio (SNR) is contained within a small subset of channels, allowing channels that contribute relatively little to SNR to be eliminated. For example, digital mode-mixing schemes employing eigen-decomposition of singular value decomposition (SVD) are often used to compress images without significant losses in image quality. These digital mode-mixing strategies provide reduced computational burden during image reconstruction and may be optimized for a given array. Moreover, digital mode-mixing strategies allow the computation of multiple mode-mixing matrices to optimize different acquisitions, for example, unaccelerated SNR or SNR in an accelerated image with different rates or different acceleration directions. However, digital mode-mixing strategies cannot reduce MRI system receiver channel requirements or the data bus bottleneck between the digital receiver and the reconstruction computer.
Hardware-based compression of acquired MR data has been provided for specific coil arrays. For example, a traditional hardware-based, mode-mixing strategy for compressing MR data utilizes degenerate birdcage coils. Unlike high-pass or low-pass birdcage coils, the modes of oscillation of a degenerate birdcage coil are resonant at the Larmor frequency and the spatial patterns of the birdcage modes are orthogonal. Therefore, decomposition is not needed to diagonalize the sensitivity correlation matrix. Furthermore, the spatial modes have very unequal weights. The commonly used “uniform mode” contains the most sensitivity while “gradient modes” contain a null in the center and a monotonic increasing sensitivity towards the periphery. Importantly, half the modes have the correct circular polarization for MR detection, so-called “CP modes,” while the other modes, the “anti-CP modes,” have the incorrect polarization and contribute no new information. Since the anti-CP modes contribute nothing, they can be excluded with little penalty, thereby allowing the benefits of a 2N-channel coil with N receivers.
It has been shown that typical head coil arrays having a single ring of loop coils on a cylindrical frame show this type of degenerative birdcage symmetry. A hardware implementation of the complete mode-mixing matrix needed for forming birdcage modes from a cylindrical ring of loop elements has recently been developed. Birdcage modes are formed by sending equal amplitude currents to each element around a cylindrical ring with a phase relationship that varies in uniform steps from zero to 2π for the uniform mode, from zero to 4π for the first gradient mode, and so forth. As a result, the mode-mixing matrix resembles a Fourier transform. This technique was implemented for radar using quadrature hybrids and phase shifters by Butler in 1961. This “Butler matrix” and mode truncation have recently been used to capture the majority of the benefit of a 7T 16-element stripline transmit array using an 8-channel parallel transmit array.
Siemens has implemented another hardware-based mode compression scheme in its total imaging matrix (TIM) RF system, which utilizes a reduced and local implementation of the birdcage basis set. In this system, data acquired by clusters of three coils is combined locally before being sent to the receivers. TIM is a registered trademark of Siemens Aktiengesellschaft of Germany. Groups of three adjacent coils are combing using a 3×3 mode-mixing matrix with fixed values, which utilizes the phase relationship that these elements would have if they formed a sub-section of a cylindrical ring and the first three birdcage modes were desired. Thus, the three loop coils are transformed into a local approximation of the birdcage modes. These modes include the “primary mode,” or CP-mode, in which the loops are added with the phase relation of the uniform birdcage mode; the “secondary mode” which utilizes the phases of the first gradient mode; and the anti-CP mode. To the extent that the cylindrical birdcage symmetry holds, the primary mode contains most of the sensitivity while the secondary mode holds some sensitivity near the periphery, and the anti-CP mode contributes relatively little. Therefore, if a large coil array is used with an instrument having a reduced number of receivers or if faster reconstruction is desired, only the primary mode or the primary and secondary modes are connected to the receivers.
While highly advantageous for specific coil arrays, such as cylindrically symmetric birdcage coils, traditional hardware-based mode-mixing strategies are more limited for general coil arrays. For example, the Butler matrix combination is inapplicable when coils are distributed along the direction of the principal magnetic field (B0). These limitations ultimately prevent the use of hardware-based MR data compression for a wide variety of applications. Furthermore, software-based mode-mixing strategies cannot reduce receiver channel requirements in MRI systems or data bottlenecks on the bus that conveys acquired scan data to an image reconstruction computer
It would therefore be desirable to develop a system and method for mode-mixing that reduces RF receiver requirements for MRI scanners, data bus bottlenecks, and the computational burden of image reconstruction.